This Graph here shows off the total rounds played of each player

ggplot(pstats, aes(y=TotalRoundsPlayed,x=Player_Name, fill=Player_Name)) + geom_bar(stat = "Identity") + geom_text(aes(label=TotalRoundsPlayed), vjust=1.6, color="white", size=3.5) + ggtitle("Total Rounds Played") +
  theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5))

overallPStats %>% filter(stat=="Rounds") %>% ggplot(aes(x=role,y=overall,fill=role))+geom_bar(stat = "Identity")  + geom_text(aes(label=overall), vjust=1.6, color="black", size=3.5) + ggtitle("Rounds played as each role")

overallPStats %>% filter(stat=="Rounds" & role!="Innocent") %>% ggplot(aes(x="",y=overall/4715,fill=role)) + 
  geom_bar(stat="identity", width=1,color="white") +
  coord_polar("y", start=0) +
  theme_void()

avgWin %>% ggplot(aes(x=role,y=winPercent,fill=role))+geom_bar(stat = "Identity")+geom_text(aes(label=sprintf("%0.2f", round(winPercent, digits = 2))), vjust=1.6, color="black", size=3.5)

ggplot(pstats,aes(x=TraitorWins, y=TraitorRounds, fill=Player_Name)) + geom_point() + ggtitle("Traitor Rounds vs. Traitor wins") +
  geom_label_repel(aes(label = Player_Name),
                  box.padding   = 0.35, 
                  point.padding = 0.5,
                  segment.color = 'grey50') +
  theme_classic()

pstats %>% group_by(Player_Name,TraitorRounds,TraitorWins) %>% 
  summarise(winPercent=TraitorWins/TraitorRounds) %>%
  arrange(desc(winPercent))
## `summarise()` has grouped output by 'Player_Name', 'TraitorRounds'. You can override using the `.groups` argument.
## # A tibble: 10 × 4
## # Groups:   Player_Name, TraitorRounds [10]
##    Player_Name     TraitorRounds TraitorWins winPercent
##    <fct>                   <dbl>       <dbl>      <dbl>
##  1 nuzzels                   145          89      0.614
##  2 silentscout9909           128          58      0.453
##  3 Amazingbro17               94          37      0.394
##  4 King Nuggets               92          32      0.348
##  5 FunLiberal                 91          31      0.341
##  6 ghostNINJA-72              83          24      0.289
##  7 MrBoy                      87          24      0.276
##  8 Jeff_the_Shark             89          24      0.270
##  9 TARTARI                    24           6      0.25 
## 10 TheMathGeek_314            62          13      0.210
pstats %>% group_by(Player_Name) %>% summarise(percentPlayed=TraitorRounds/TotalRoundsPlayed) %>% 
  ggplot(aes(y=percentPlayed,x=Player_Name,fill=Player_Name)) + geom_bar(stat = "Identity") + ggtitle("Percent of rounds played as traitor") +
  theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5))

ggplot(pstats,aes(x=InnocentWins, y=InnocentRounds, fill=Player_Name)) + geom_point() + ggtitle("Innocent Rounds vs. Innocent wins") +
  geom_label_repel(aes(label = Player_Name),
                  box.padding   = 0.35, 
                  point.padding = 0.5,
                  segment.color = 'grey50') +
  theme_classic()

pstats %>% group_by(Player_Name,InnocentRounds,InnocentWins) %>% 
  summarise(winPercent=InnocentWins/InnocentRounds) %>%
  arrange(desc(winPercent))
## `summarise()` has grouped output by 'Player_Name', 'InnocentRounds'. You can override using the `.groups` argument.
## # A tibble: 10 × 4
## # Groups:   Player_Name, InnocentRounds [10]
##    Player_Name     InnocentRounds InnocentWins winPercent
##    <fct>                    <dbl>        <dbl>      <dbl>
##  1 silentscout9909            286          187      0.654
##  2 Amazingbro17               258          166      0.643
##  3 nuzzels                    316          201      0.636
##  4 Jeff_the_Shark             265          168      0.634
##  5 FunLiberal                 265          165      0.623
##  6 TARTARI                     67           40      0.597
##  7 MrBoy                      254          151      0.594
##  8 King Nuggets               259          153      0.591
##  9 ghostNINJA-72              213          123      0.577
## 10 TheMathGeek_314            191          108      0.565
pstats %>% group_by(Player_Name) %>% summarise(percentPlayed=InnocentRounds/TotalRoundsPlayed) %>% 
  ggplot(aes(y=percentPlayed,x=Player_Name,fill=Player_Name)) + geom_bar(stat = "Identity") + ggtitle("Percent of rounds played as innocent") +
  theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5))+geom_text(aes(label=sprintf("%0.2f", round(percentPlayed, digits = 2))), vjust=1.6, color="black", size=3.5)

ggplot(pstats,aes(x=DetectiveWins, y=DetectiveRounds, fill=Player_Name)) + geom_point() + ggtitle("Detective Rounds vs. Detective wins") +
  geom_label_repel(aes(label = Player_Name),
                  box.padding   = 0.35, 
                  point.padding = 0.5,
                  segment.color = 'grey50') +
  theme_classic()

pstats %>% group_by(Player_Name,DetectiveRounds,DetectiveWins) %>% 
  summarise(winPercent=DetectiveWins/DetectiveRounds) %>%
  arrange(desc(winPercent))
## `summarise()` has grouped output by 'Player_Name', 'DetectiveRounds'. You can override using the `.groups` argument.
## # A tibble: 10 × 4
## # Groups:   Player_Name, DetectiveRounds [10]
##    Player_Name     DetectiveRounds DetectiveWins winPercent
##    <fct>                     <dbl>         <dbl>      <dbl>
##  1 nuzzels                      88            62      0.705
##  2 King Nuggets                 59            38      0.644
##  3 ghostNINJA-72                49            30      0.612
##  4 Amazingbro17                 74            45      0.608
##  5 TARTARI                      12             7      0.583
##  6 MrBoy                        69            40      0.580
##  7 TheMathGeek_314              42            24      0.571
##  8 silentscout9909              73            41      0.562
##  9 Jeff_the_Shark               66            36      0.545
## 10 FunLiberal                   61            29      0.475
pstats %>% group_by(Player_Name) %>% summarise(percentPlayed=DetectiveRounds/TotalRoundsPlayed) %>% 
  ggplot(aes(y=percentPlayed,x=Player_Name,fill=Player_Name)) + geom_bar(stat = "Identity") + ggtitle("Percent of rounds played as detective") +
  theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5))+geom_text(aes(label=sprintf("%0.2f", round(percentPlayed, digits = 2))), vjust=1.6, color="black", size=3.5)

ggplot(pstats,aes(x=Zombiewins, y=ZombieRounds, fill=Player_Name)) + geom_point() + ggtitle("Zombie Rounds vs. Zombie wins") +
  geom_label_repel(aes(label = Player_Name),
                  box.padding   = 0.35, 
                  point.padding = 0.5,
                  segment.color = 'grey50') +
  theme_classic()

pstats %>% group_by(Player_Name,Zombiewins,ZombieRounds) %>% 
  summarise(winPercent=Zombiewins/ZombieRounds) %>%
  arrange(desc(winPercent))
## `summarise()` has grouped output by 'Player_Name', 'Zombiewins'. You can override using the `.groups` argument.
## # A tibble: 10 × 4
## # Groups:   Player_Name, Zombiewins [10]
##    Player_Name     Zombiewins ZombieRounds winPercent
##    <fct>                <dbl>        <dbl>      <dbl>
##  1 nuzzels                 50           64      0.781
##  2 MrBoy                   18           29      0.621
##  3 TheMathGeek_314         14           23      0.609
##  4 Jeff_the_Shark          22           37      0.595
##  5 Amazingbro17            16           29      0.552
##  6 ghostNINJA-72           11           20      0.55 
##  7 FunLiberal              17           31      0.548
##  8 silentscout9909         25           46      0.543
##  9 King Nuggets            14           29      0.483
## 10 TARTARI                  2            6      0.333
pstats %>% group_by(Player_Name) %>% summarise(percentPlayed=ZombieRounds/TotalRoundsPlayed) %>% 
  ggplot(aes(y=percentPlayed,x=Player_Name,fill=Player_Name)) + geom_bar(stat = "Identity") + ggtitle("Percent of rounds played as zombie") +
  theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5))+geom_text(aes(label=sprintf("%0.2f", round(percentPlayed, digits = 2))), vjust=1.6, color="black", size=3.5)

ggplot(pstats,aes(x=HypnotistWins, y=HypnotistRounds, fill=Player_Name)) + geom_point() + ggtitle("Hypnotist Rounds vs. Hypnotist wins") +
  geom_label_repel(aes(label = Player_Name),
                  box.padding   = 0.35, 
                  point.padding = 0.5,
                  segment.color = 'grey50') +
  theme_classic()

pstats %>% group_by(Player_Name,HypnotistWins,HypnotistRounds) %>% 
  summarise(winPercent=HypnotistWins/HypnotistRounds) %>%
  arrange(desc(winPercent))
## `summarise()` has grouped output by 'Player_Name', 'HypnotistWins'. You can override using the `.groups` argument.
## # A tibble: 10 × 4
## # Groups:   Player_Name, HypnotistWins [10]
##    Player_Name     HypnotistWins HypnotistRounds winPercent
##    <fct>                   <dbl>           <dbl>      <dbl>
##  1 Amazingbro17                9              11      0.818
##  2 nuzzels                    16              21      0.762
##  3 ghostNINJA-72               5              10      0.5  
##  4 silentscout9909             4               9      0.444
##  5 MrBoy                       6              14      0.429
##  6 Jeff_the_Shark              6              20      0.3  
##  7 TheMathGeek_314             2               7      0.286
##  8 FunLiberal                  3              12      0.25 
##  9 King Nuggets                3              13      0.231
## 10 TARTARI                     1               9      0.111
pstats %>% group_by(Player_Name) %>% summarise(percentPlayed=HypnotistRounds/TotalRoundsPlayed) %>% 
  ggplot(aes(y=percentPlayed,x=Player_Name,fill=Player_Name)) + geom_bar(stat = "Identity") + ggtitle("Percent of rounds played as hypnotist") +
  theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5))+geom_text(aes(label=sprintf("%0.2f", round(percentPlayed, digits = 2))), vjust=1.6, color="black", size=3.5)

ggplot(pstats,aes(x=Player_Name, y=CrookedCop, fill=Player_Name)) + geom_bar(stat="identity") + geom_text(aes(label=CrookedCop), vjust=1.6, color="white", size=3.5) + ggtitle("Amount of Innocents killed as detective") +
  theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5))

pstats %>% group_by(Player_Name, DetectiveRounds, CrookedCop) %>% 
  summarise(Crookedeness=CrookedCop/DetectiveRounds) %>%
  arrange(desc(Crookedeness))
## `summarise()` has grouped output by 'Player_Name', 'DetectiveRounds'. You can override using the `.groups` argument.
## # A tibble: 10 × 4
## # Groups:   Player_Name, DetectiveRounds [10]
##    Player_Name     DetectiveRounds CrookedCop Crookedeness
##    <fct>                     <dbl>      <dbl>        <dbl>
##  1 ghostNINJA-72                49         31        0.633
##  2 TARTARI                      12          6        0.5  
##  3 King Nuggets                 59         29        0.492
##  4 FunLiberal                   61         23        0.377
##  5 TheMathGeek_314              42         11        0.262
##  6 Amazingbro17                 74         18        0.243
##  7 Jeff_the_Shark               66         13        0.197
##  8 MrBoy                        69         13        0.188
##  9 silentscout9909              73          9        0.123
## 10 nuzzels                      88         10        0.114
ggplot(pstats,aes(x=Player_Name, y=TriggerHappyInnocent, fill=Player_Name)) + geom_bar(stat="identity") + geom_text(aes(label=TriggerHappyInnocent), vjust=1.6, color="white", size=3.5) + ggtitle("Amount of Innocents killed as an Innocent") +
  theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5))

pstats %>% group_by(Player_Name, InnocentRounds, TriggerHappyInnocent) %>% 
  summarise(Shootyness=TriggerHappyInnocent/InnocentRounds) %>%
  arrange(desc(Shootyness))
## `summarise()` has grouped output by 'Player_Name', 'InnocentRounds'. You can override using the `.groups` argument.
## # A tibble: 10 × 4
## # Groups:   Player_Name, InnocentRounds [10]
##    Player_Name     InnocentRounds TriggerHappyInnocent Shootyness
##    <fct>                    <dbl>                <dbl>      <dbl>
##  1 ghostNINJA-72              213                   83     0.390 
##  2 King Nuggets               259                   71     0.274 
##  3 MrBoy                      254                   62     0.244 
##  4 FunLiberal                 265                   60     0.226 
##  5 nuzzels                    316                   56     0.177 
##  6 silentscout9909            286                   50     0.175 
##  7 TARTARI                     67                   11     0.164 
##  8 Jeff_the_Shark             265                   34     0.128 
##  9 Amazingbro17               258                   33     0.128 
## 10 TheMathGeek_314            191                   19     0.0995
ggplot(pstats,aes(x=Player_Name,y=KilledFirst,fill=Player_Name)) + geom_bar(stat = "Identity")  + geom_text(aes(label=KilledFirst), vjust=1.6, color="Blue", size=3.5) + ggtitle("Amount of times killed first") +
  theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5))

ggplot(pstats, aes(x=Player_Name, y=KilledFirst/TotalRoundsPlayed, fill=Player_Name)) + 
  geom_bar(stat="identity") + ggtitle("Percentage of times killed first") +
  theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5))+geom_text(aes(label=sprintf("%0.2f", round(KilledFirst/TotalRoundsPlayed, digits = 2))), vjust=1.6, color="black", size=3.5)

ggplot(pstats,aes(x=Player_Name,y=TotalFallDamage, fill=Player_Name)) + geom_bar(stat = "Identity")  + geom_text(aes(label=TotalFallDamage), vjust=1.6, color="Blue", size=3.5) + ggtitle("People least safe near ledges") +
  theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5))

dequipClean %>% group_by(stat) %>% summarise(overallTimesBought = sum(count)) %>%
  ggplot(aes(x=stat,y=overallTimesBought)) + geom_bar(stat="Identity") + geom_text(aes(label=overallTimesBought), vjust=1.6, color="Red", size=2.5) + ggtitle("Times each detective equipment was bought") +
  theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5))

dequipClean %>% group_by(stat) %>% summarise(percentBought=sum(count)/1172) %>% arrange(desc(percentBought))
## # A tibble: 35 × 2
##    stat           percentBought
##    <chr>                  <dbl>
##  1 Gold_Deagle           0.153 
##  2 Body_Armor            0.119 
##  3 T-Suitcase            0.0811
##  4 PHD                   0.0708
##  5 Radar                 0.0597
##  6 Command_Prompt        0.0546
##  7 Speed_Cola            0.0546
##  8 DoubleTap             0.0495
##  9 Mystery_Box           0.0418
## 10 Stamin-up             0.0341
## # … with 25 more rows
dequipClean %>% filter(count>0) %>% group_by(Player_Name, stat) %>% summarise(equipBought=n()) %>% 
  ggplot(aes(x=Player_Name,y=equipBought, fill=Player_Name)) + geom_bar(stat = "Identity")  +
  theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5)) + 
  ggtitle("Number of different detective equipment bought")
## `summarise()` has grouped output by 'Player_Name'. You can override using the `.groups` argument.

dequipClean %>% ggplot(aes(x=Player_Name,y=count, fill=Player_Name)) + geom_bar(stat = "Identity") + facet_wrap(~stat, ncol=5, scales = "free_y") +
  theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5))

tequipClean %>% group_by(stat) %>% summarise(overallTimesBought = sum(count)) %>%
  ggplot(aes(x=stat,y=overallTimesBought)) + geom_bar(stat="Identity") + geom_text(aes(label=overallTimesBought), vjust=1.6, color="Red", size=2.5) + ggtitle("Times each traitor equipment was bought") +
  theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5))

tequipClean %>% group_by(stat) %>% summarise(percentBought=sum(count)/1172) %>% arrange(desc(percentBought))
## # A tibble: 72 × 2
##    stat         percentBought
##    <chr>                <dbl>
##  1 Body_Armor          0.374 
##  2 Radar               0.192 
##  3 PHD                 0.116 
##  4 Eagleflight         0.110 
##  5 wallhack            0.104 
##  6 Speed_Cola          0.0990
##  7 DoubleTap           0.0973
##  8 Antlion             0.0887
##  9 Katana              0.0785
## 10 Silenced_AWP        0.0776
## # … with 62 more rows
tequipClean %>% filter(count>0) %>% group_by(Player_Name, stat) %>% summarise(equipBought=n()) %>% 
  ggplot(aes(x=Player_Name,y=equipBought, fill=Player_Name)) + geom_bar(stat = "Identity")  +
  theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5)) + 
  ggtitle("Number of different traitor equipment bought")
## `summarise()` has grouped output by 'Player_Name'. You can override using the `.groups` argument.

tequipClean %>% ggplot(aes(x=Player_Name,y=count, fill=Player_Name)) + geom_bar(stat = "Identity") + facet_wrap(~stat, ncol=6, scales = "free_y") +
  theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5))